QUIDS: Quality-informed Incentive-driven Multi-agent Dispatching System for Mobile Crowdsensing

2025-12-18

AI Summary

In non-dedicated vehicle-based mobile crowdsensing (NVMCS), optimizing information quality (QoI)—characterized by sensing coverage, reliability, and dynamic vehicle participation—is challenging due to their strong coupling under uncertainty. Method: This paper proposes a belief-driven framework for joint cooperative scheduling and incentive allocation. It introduces the novel Aggregate Sensing Quality (ASQ) metric, integrating Bayesian belief updating, multi-agent cooperative scheduling, and quality-aware incentive modeling to jointly optimize coverage and reliability under uncertainty. Contribution/Results: Under budget constraints, ASQ achieves a 38% improvement over an unscheduled baseline and a 10% gain over state-of-the-art methods. Moreover, map reconstruction error is reduced by 39–74%, significantly enhancing cost-effective urban sensing performance.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Learning to Search
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

This paper addresses the challenge of achieving optimal Quality of Information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) systems. The key obstacles are the interrelated issues of sensing coverage, sensing reliability, and the dynamic participation of vehicles. To tackle these, we propose QUIDS, a QUality-informed Incentive-driven multi-agent Dispatching System, which ensures high sensing coverage and reliability under budget constraints. QUIDS introduces a novel metric, Aggregated Sensing Quality (ASQ), to quantitatively capture QoI by integrating both coverage and reliability. We also develop a Mutually Assisted Belief-aware Vehicle Dispatching algorithm that estimates sensing reliability and allocates incentives under uncertainty, further improving ASQ. Evaluation using real-world data from a metropolitan NVMCS deployment shows QUIDS improves ASQ by 38% over non-dispatching scenarios and by 10% over state-of-the-art methods. It also reduces reconstruction map errors by 39-74% across algorithms. By jointly optimizing coverage and reliability via a quality-informed incentive mechanism, QUIDS enables low-cost, high-quality urban monitoring without dedicated infrastructure, applicable to smart-city scenarios like traffic and environmental sensing.

Problem

• Optimizes sensing coverage and reliability in non-dedicated vehicular crowdsensing systems.
• Develops a quality-informed incentive mechanism to manage dynamic vehicle participation.
• Enables cost-effective urban monitoring without dedicated infrastructure for smart cities.

Innovation

• Introduces Aggregated Sensing Quality metric for coverage and reliability
• Develops belief-aware dispatching algorithm to allocate incentives under uncertainty
• Uses quality-informed incentive mechanism to jointly optimize coverage and reliability